In [1]:
import pandas as pd
import plotly.io as pio
import plotly.graph_objects as go
pio.templates.default = "plotly_white"
import plotly.express as px

data = pd.read_csv("Watch_Fitness data.csv")
print(data.head())

         Date       Time  Step Count  Distance  Energy Burned  \
0  2023-03-21  16:01:23           46   0.02543         14.620   
1  2023-03-21  16:18:37          645   0.40041         14.722   
2  2023-03-21  16:31:38           14   0.00996         14.603   
3  2023-03-21  16:45:37           13   0.00901         14.811   
4  2023-03-21  17:10:30           17   0.00904         15.153   

   Flights Climbed  Walking Double Support Percentage  Walking Speed  
0                3                              0.304          3.060  
1                3                              0.309          3.852  
2                4                              0.278          3.996  
3                3                              0.278          5.040  
4                3                              0.281          5.184
In [2]:
print(data.isnull().sum())

Date                                 0
Time                                 0
Step Count                           0
Distance                             0
Energy Burned                        0
Flights Climbed                      0
Walking Double Support Percentage    0
Walking Speed                        0
dtype: int64
In [3]:
fig1 = px.line(data, x="Time",
               y="Step Count",
               title="Step Count Over Time")
fig1.show()
In [4]:
fig2 = px.line(data, x="Time",
               y="Distance",
               title="Distance Covered Over Time")
fig2.show()
In [5]:
fig3 = px.line(data, x="Time",
               y="Energy Burned",
               title="Energy Burned Over Time")
fig3.show()
In [6]:
fig4 = px.line(data, x="Time",
               y="Walking Speed",
               title="Walking Speed Over Time")
fig4.show()
In [7]:
average_step_count_per_day = data.groupby("Date")["Step Count"].mean().reset_index()

fig5 = px.bar(average_step_count_per_day, x="Date",
              y="Step Count",
              title="Average Step Count per Day")
fig5.update_xaxes(type='category')
fig5.show()
In [8]:
data["Walking Efficiency"] = data["Distance"] / data["Step Count"]

fig6 = px.line(data, x="Time",
               y="Walking Efficiency",
               title="Walking Efficiency Over Time")
fig6.show()
In [9]:
time_intervals = pd.cut(pd.to_datetime(data["Time"]).dt.hour,
                        bins=[0, 12, 18, 24],
                        labels=["Morning", "Afternoon", "Evening"], 
                        right=False)

data["Time Interval"] = time_intervals

# Variations in Step Count and Walking Speed by Time Interval
fig7 = px.scatter(data, x="Step Count",
                  y="Walking Speed",
                  color="Time Interval",
                  title="Step Count and Walking Speed Variations by Time Interval",
                  trendline='ols')
fig7.show()
In [10]:
daily_avg_metrics = data.groupby("Date").mean().reset_index()

daily_avg_metrics_melted = daily_avg_metrics.melt(id_vars=["Date"], 
                                                  value_vars=["Step Count", "Distance", 
                                                              "Energy Burned", "Flights Climbed", 
                                                              "Walking Double Support Percentage", 
                                                              "Walking Speed"])

# Treemap of Daily Averages for Different Metrics Over Several Weeks
fig = px.treemap(daily_avg_metrics_melted,
                 path=["variable"],
                 values="value",
                 color="variable",
                 hover_data=["value"],
                 title="Daily Averages for Different Metrics")
fig.show()
In [11]:
metrics_to_visualize = ["Distance", "Energy Burned", "Flights Climbed", 
                        "Walking Double Support Percentage", "Walking Speed"]

# Reshape data for treemap
daily_avg_metrics_melted = daily_avg_metrics.melt(id_vars=["Date"], value_vars=metrics_to_visualize)

fig = px.treemap(daily_avg_metrics_melted,
                 path=["variable"],
                 values="value",
                 color="variable",
                 hover_data=["value"],
                 title="Daily Averages for Different Metrics (Excluding Step Count)")
fig.show()
In [ ]: